Publikationen von Henning Pohl

Emoji, a set of pictographic Unicode characters, have seen strong uptake over the last couple of years. All common mobile platforms and many desktop systems now support emoji entry and users have embraced their use. Yet, we currently know very little about what makes for good emoji entry. While soft keyboards for text entry are well optimized, based on language and touch models, no such information exists to guide the design of emoji keyboards. In this article, we investigate of the problem of emoji entry, starting with a study of the current state of the emoji keyboard implementation in Android. To enable moving forward to novel emoji keyboard designs, we then explore a model for emoji similarity that is able to inform such designs. This semantic model is based on data from 21 million collected tweets containing emoji. We compare this model against a solely description-based model of emoji in a crowdsourced study. Our model shows good performance in capturing detailed relationships between emoji.

Compression feedback uses inflatable straps to create uniform pressure sensations around limbs. Lower-pressure stimuli are well suited as a feedback channel for, e.g., notifications. However, operating compression feedback systems at higher pressure levels allows to physically inhibit movement. Here, we describe this modality and present a pervasive jogging game that employs physical inhibition to push runners to reach checkpoints in time.

Current mobile devices commonly use vibration feedback to signal incoming notifications. However, vibration feedback exhibits strong attention capture, limiting its use to short periods and prominent notifications. Instead, we investigate the use of compression feedback for notifications, which scales from subtle stimuli to strong ones and can provide sustained stimuli over longer periods. Compression feedback utilizes inflatable straps around a user's limbs, a form factor allowing for easy integration into many common wearables. We explore technical aspects of compression feedback and investigate its psychophysical properties with several lab and in situ studies. Furthermore, we show how compression feedback enables reactive feedback. Here, deflation patterns are used to reveal further information on a user's query. We also compare compression and vibrotactile feedback and find that they have similar performance.

Interaction with mobile devices currently requires close engagement with them. For example, users need to pick them up and unlock them, just to check whether the last notification was for an urgent message. But such close engagement is not always desirable, e.g., when working on a project with the phone just laying around on the table. Instead, we explore around-device interactions to bring up and control notifications. As users get closer to the device, more information is revealed and additional input options become available. This allows users to control how much they want to engage with the device. For feedback, we use a custom LED-matrix display prototype on the edge of the device. This allows for coarse, but bright, notifications in the periphery of attention, but scales up to allow for slightly higher resolution feedback as well.

Current soft keyboards for emoji entry all present emoji in the same way: in long lists, spread over several categories. While categories limit the number of emoji in each individual list, the overall number is still so large, that emoji entry is a challenging task. The task takes particularly long if users pick the wrong category when searching for an emoji. Instead, we propose a new zooming keyboard for emoji entry. Here, users can see all emoji at once, aiding in building spatial memory where related emoji are to be found. We compare our zooming emoji keyboard against the Google keyboard and find that our keyboard allows for 18% faster emoji entry, reducing the required time for one emoji from 15.6s to 12.7s. A preliminary longitudinal evaluation with three participants showed that emoji entry time over the duration of the study improved at up to 60% to a final average of 7.5s.

With the increasing popularity of smartwatches over the last years, there has been a substantial interest in novel input methods for such small devices. However, feedback modalities for smartwatches have not seen the same level of interest. This is surprising, as one of the primary function of smartwatches is their use for notifications. It is the interrupting nature of current notifications on smartwatches that has also drawn some of the more critical responses to them. Here, we present a subtle notification mechanism for smartwatches that uses light scattering in a wearer's skin as a feedback modality. This does not disrupt the wearer in the same way as vibration feedback and also connects more naturally with the user's body.

In what we call the focused-casual continuum, users pick how much control they want to have when interacting. Through offering several different ways for interaction, such interfaces can then be more appropriate for, e.g., use in some social situations, or use when exhausted. In a very basic example, an alarm clock could offer one interaction mode where an alarm can only be turned off, while in another, users can choose between different snooze responses. The first mode is more restrictive but could be controlled with one coarse gesture. Only when the user wishes to pick between several responses, more controlled and fine interaction is needed. Low control, more casual interactions can take place in the background or the periphery of the user, while focused interactions move into the foreground. Along the focused-casual continuum, a plethora of interaction techniques have their place. Currently, focused interaction techniques are often the default ones. In this chapter, we thus focus more closely on techniques for casual interaction, which offer ways to interact with lower levels of control. Presented use cases cover scenarios such as text entry, user recognition, tangibles, or steering tasks. Furthermore, in addition to potential benefits from applying casual interaction techniques during input, there is also a need for feedback which does not immediately grab our attention, but can scale from the periphery to the focus of our attention. Thus, we also cover several such feedback methods and show how the focused-casual continuum can encompass the whole interaction.

Plagiarism in online learning environments has a detrimental effect on the trust of online courses and their viability. Automatic plagiarism detection systems do exist yet the specific situation in online courses restricts their use. To allow for easy automated grading, online assignments usually are less open and instead require students to fill in small gaps. Therefore solutions tend to be very similar, yet are then not necessarily plagiarized. In this paper we propose a new approach to detect code re-use that increases the prediction accuracy by dynamically removing parts in assignments which are part of almost every assignment—the so called common ground. Our approach shows significantly better F-measure and Cohen's Kappa results than other state of the art algorithms such as Moss or JPlag. The proposed method is also language agnostic to the point that training and test data sets can be taken from different programming languages.

Many MOOCs report high drop off rates for their students. Among the factors reportedly contributing to this picture are lack of motivation, feelings of isolation, and lack of interactivity in MOOCs. This paper investigates the potential of gamification with social game elements for increasing retention and learning success. Students in our experiment showed a significant increase of 25% in retention period (videos watched) and 23% higher average scores when the course interface was gamified. Social game elements amplify this effect significantly – students in this condition showed an increase of 50% in retention period and 40% higher average test scores.

In relaxed living room settings, using a phone to control the room can be inappropriate or cumbersome. Instead of such explicit interactions, we enable implicit control via a posture-sensing couch. Users can then, e.g., automatically turn on the reading lights when sitting down.

We present a novel way to recognize users by the way they press a button. Our approach allows low-effort and fast interaction without the need for augmenting the user or controlling the environment. It eschews privacy concerns of methods such as fingerprint scanning. Button pressing behavior is sufficiently discriminative to allow distinguishing users within small groups. This approach combines recognition and action in a single step, e.g., getting and tallying a coffee can be done with one button press. We deployed our system for 5 users over a period of 4 weeks and achieved recognition rates of 95% in the last week. We also ran a larger scale but short-term evaluation to investigate effects of group size and found that our method degrades gracefully for larger groups.

In the focused-casual continuum, users are given a choice of how much they wish to engage with an interface. In situations where they are, e.g., physically encumbered, they may wish to trade some control for the convenience of interacting at all. Currently, most devices only offer focused interaction capabilities or restrict users to binary foreground/background interaction choices. In casual interactions, users consciously pick a way to interact that is suitable for their desired engagement level. Users will be expecting devices to offer several ways for control along the engagement scale.

Most common forms of haptic feedback use vibration, which immediately captures the user's attention, yet is limited in the range of strengths it can achieve. Vibration feedback over extended periods also tends to be annoying. We present compression feedback, a form of haptic feedback that scales from very subtle to very strong and is able to provide sustained stimuli and pressure patterns. The demonstration may serve as an inspiration for further work in this area, applying compression feedback to generate subtle, intimate, as well as intense feedback.

For many people their phones have become their main everyday tool. While phones can fulfill many different roles they also require users to (1) make do with affordance not specialized for the specific task, and (2) closely engage with the device itself. We propose utilizing the space and objects around the phone to offer better task affordance and to create an opportunity for casual interactions. Such around-device devices are a class of interactors that do not require users to bring special tangibles, but repurpose items already found in the user's surroundings. In a survey study, we determine which places and objects are available to around-device devices. Furthermore, in an elicitation study, we observe what objects users would use for ten interactions.

In the area of sports, athletes often resort to performance enhancing drugs to gain an advantage. Similarly, people use pharmaceutical drugs to aid learning, dexterity, or concentration. We investigate how pharmaceutical drugs could be used to enhance interactions. We envision that in the future, people might take pills along with their vitamins in the morning to improve how they can interact over the day. In addition to performance improvements this, e.g., could also include improvements in enjoyment or fatigue.

When interacting casually, users relinquish some control over their interaction to gain the freedom to devote their engagement elsewhere. This allows them to still interact even when they are encumbered, distracted, or engaging with others. With their focus on something else, casual interaction will often take place in the periphery---either spatially by, e.g., interacting laterally or with respect to attention, by interacting in the background.

We present imaginary reality basketball, i.e., a ball game that mimics the respective real world sport, i.e., basketball, except that there is no visible ball. The ball is virtual and players learn about its position only from watching each other act and a small amount of occasional auditory feedback, e.g., when a person is receiving the ball. Imaginary reality games maintain many of the properties of physical sports, such as unencumbered play, physical exertion, and immediate social interaction between players. At the same time, they allow introducing game elements from video games, such as power-ups, non-realistic physics, and player balancing. Most importantly, they create a new game dynamic around the notion of the invisible ball.

Modern mobile devices typically rely on touchscreen keyboards for input. Unfortunately, users often struggle to enter text accurately on virtual keyboards. To address this, we present a novel decoder for touchscreen text entry that combines probabilistic touch models with a long-span language model. We investigate two touch models – one based on Gaussian Processes that implicitly models the inherent uncertainty of the touching process and a second that allows users to explicitly control the uncertainity via touch pressure. Using the first model we show that character error rate can be reduced by up to 7% over a baseline, and by up to 1.3% over a leading commercial keyboard. With the second model, we demonstrate that providing users with control over input certainty results in improved text entry rates for phrases containing out of vocabulary words.

We present imaginary reality games, i.e., games that mimic the respective real world sport, such as basketball or soccer, except that there is no visible ball. The ball is virtual and players learn about its position only from watching each other act and a small amount of occasional auditory feedback, e.g., when a person is receiving the ball. Imaginary reality games maintain many of the properties of physical sports, such as unencumbered play, physical exertion, and immediate social interaction between players. At the same time, they allow introducing game elements from video games, such as power-ups, non-realistic physics, and player balancing. Most importantly, they create a new game dynamic around the notion of the invisible ball. To allow players to successfully interact with the invisible ball, we have created a physics engine that evaluates all plausible ball trajectories in parallel, allowing the game engine to select the trajectory that leads to the most enjoyable game play while still favoring skillful play.

We describe the focused–casual continuum, a framework for describing interaction techniques according to the degree to which they allow users to adapt how much attention and effort they choose to invest in an interaction conditioned on their current situation. Casual interactions are particularly appropriate in scenarios where full engagement with devices is frowned upon socially, is unsafe, physically challenging or too mentally taxing. Novel sensing approaches which go beyond direct touch enable wider use of casual interactions, which will often be ‘around device’ interactions. We consider the degree to which previous commercial products and research prototypes can be considered as fitting the focused– casual framework, and describe the properties using control theoretic concepts. In an experimental study we observe that users naturally apply more precise and more highly engaged interaction techniques when faced with a more challenging task and use more relaxed gestures in easier tasks.

We present Quantum games, physical games that resemble corresponding real–world sports—except that the ball exists only in the players’ imagination. We demonstrate Quantum versions of team handball and air hockey. A computer system keeps score by tracking players using a Microsoft Kinect (air hockey) or a webcam (handball), simulates the physics of the ball, and reports ball interactions and scores back using auditory feedback. The key element that makes Quantum games playable is a novel type of physics engine that evaluates not one, but samples the set of all plausible ball trajectories in parallel. Before choosing a trajectory to realize, the engine massively increases the probability of outcomes that lead to enjoyable gameplay, such as goal shots, but also successful passes and intercepts that lead to fluid gameflow. The same mechanism allows giving a boost to inexpe- rienced players and implementing power–ups.

Advances in sensing technology are currently bringing touch input to non-planar surfaces, ranging from spherical touch screens to prototypes the size and shape of a ping-pong ball. To help interface designers create usable interfaces on such devices, we determine how touch surface curvature affects targeting. We present a user study in which participants acquired targets on surfaces of different curvature and at locations of different slope. We find that surface convexity increases pointing accuracy, and in particular reduces the offset between the input point perceived by users and the input point sensed by the device. Concave surfaces, in contrast, are subject to larger error offsets. This is likely caused by how concave surfaces hug the user's finger, thus resulting in a larger contact area. The effect of slope on targeting, in contrast, is unexpected at first sight. Some targets located downhill from the user's perspective are subject to error offsets in the opposite direction from all others. This appears to be caused by participants acquiring these targets using a different finger posture that lets them monitor the position of their fingers more effectively.

In this paper we describe a method to detect patterns in dance movements. Such patterns can be used in the context of interactive dance systems to allow dancers to influence computational systems with their body movements. For the detection of motion patterns, dynamic time warping is used to compute the distance between two given movements. A custom threshold clustering algorithm is used for subsequent unsupervised classification of movements. For the evaluation of the presented method, a wearable sensor system was built. To quantify the accuracy of the classification, a custom label space mapping was designed to allow comparison of sequences with disparate label sets.